利用python 写出ｄＳ／ｄｔ＝－β１ＳＩ－β２ＳＥ－φＳ， ｄＶ／ｄｔ＝φＳ－ １－（ ）σ （ ） β１Ｉ＋β２Ｅ Ｖ， ｄＥ／ｄｔ＝β１ＳＩ＋β２ＳＥ＋ １－（ ）σ （ ） β１Ｉ＋β２Ｅ Ｖ－αＥ， ｄＩ／ｄｔ＝αＥ－γＩ， ｄＲ／ｄｔ＝γＩ ■ ■

import numpy as np
from scipy.integrate import odeint

# set parameters
beta1 = 0.2
beta2 = 0.3
phi = 0.1
sigma = 0.1
alpha = 0.05
gamma = 0.02

# define the system of ODEs
def model(y, t):
S, V, E, I, R = y
dSdt = -beta1*S*I - beta2*S*E - phi*S
dVdt = phi*S - (1-sigma*V)*(beta1*I + beta2*E)*V
dEdt = beta1*S*I + beta2*S*E + (1-sigma*V)*(beta1*I + beta2*E)*V - alpha*E
dIdt = alpha*E - gamma*I
dRdt = gamma*I
return [dSdt, dVdt, dEdt, dIdt, dRdt]

# set initial conditions
y0 = [0.9, 0.1, 0.01, 0.001, 0]

# set time points
t = np.linspace(0, 100, 1000)

# solve the ODEs
y = odeint(model, y0, t)

# plot the results
import matplotlib.pyplot as plt
plt.plot(t, y[:,0], label='S')
plt.plot(t, y[:,1], label='V')
plt.plot(t, y[:,2], label='E')
plt.plot(t, y[:,3], label='I')
plt.plot(t, y[:,4], label='R')
plt.xlabel('Time')
plt.ylabel('Population')
plt.title('SEIR Model')
plt.legend()
plt.show()